Quantitative Analysis of Satisfaction with Chinese Local Government Digital Public Service Policies Using XGBoost Algorithm

With the development of digital technology, although local governments have been using digital means to improve the quality of public services, traditional statistical methods have limitations in processing complex, high-dimensional data and revealing factors influencing policies. This paper used the XGBoost algorithm to construct a satisfaction prediction model, leveraging its advantages in handling nonlinear relationships and feature interactions to assist government decision-making through prediction and feature analysis. This study is based on questionnaire surveys and public data, and the optimal configuration of the model was determined through preprocessing and parameter tuning. Experiments showed that the proposed model outperforms other models in terms of prediction accuracy, robustness, efficiency, and cross-scenario applicability. Through empirical analysis, this study shows that the XGBoost model has significant advantages in predicting local government digital public service policy satisfaction. Its mean square error (MSE) is only 0.056, which is 37.1% lower than the traditional linear regression model. This means that XGBoost can more accurately capture the complex nonlinear relationships that influence public satisfaction.